#Join the data boards
I loaded september to october data boards from 2017 - 2023 to represent the freshers season then I merged the health boards #I summarise total antidepressant prescriptions per Freshers year and plotted the graph to see the trend #Looked at pre coviid , during covid and after covid trend to see if there has been any impact or association
library(tidyverse)
library(here) # directory stucture
library(gt) # tables
library(janitor) # cleaning data
library(ggplot2) # plotting graph
library(sf) # to read in map data
library(readxl) # to read in map data
library(plotly) # to make interactive
library(viridis)
loading a large amount of data in a shorter time period by downloading and using the mapdfr function (data from 2017-2023)
files <- list.files(here("data", "winter_data"), pattern = "csv")
winter_data <- files %>%
map_dfr(~read_csv(here("data", "winter_data", .))) %>%
clean_names()
clean up data and filter for the sections you want
filtered_winter_data <- winter_data %>%
filter(str_starts(bnf_item_code,"0403")) %>% #antidepressant code is 0403
mutate(year = as.numeric(substr(paid_date_month,1,4)), month = as.numeric(substr(paid_date_month,5,6))) %>% #separates the date into years and month so that i can group winter sections
mutate(winter_year=case_when(month == 12 ~ year + 1,
month %in% c(1,2) ~ year) )#makes a new column to group the winter years
filtered_winter_data <- filtered_winter_data %>%
unite("healthboards",hbt2014,hbt,sep = "_")#so some of my data healthboard codes were under the name hbt_2014 AND another was hbt so i had to merge the column so all the healthboard columns fall under one
filtered_winter_data$healthboards <- gsub("[NA]","",filtered_winter_data$healthboards)
filtered_winter_data$healthboards <-
gsub("_","",filtered_winter_data$healthboards)#had to remove some NA characters and '_' characters
Graph 1
winter_years_data <- filtered_winter_data %>%
group_by(winter_year) %>%
summarise(total_items=sum(number_of_paid_items,na.rm = TRUE))
plot <- ggplot(winter_years_data,aes(x=winter_year,y=total_items)) +
geom_line(linewidth=0.7,colour = "blue") +
geom_point(size=2)+
scale_x_continuous(breaks=2017:2023) +
labs(title="Antidepressant Prescriptions During Winter Season",x="Year",y="Total Antidepressant Prescriptions") +
theme_minimal()
ggplotly(plot)
#write a code talking about the zoomed in changes and reference why you dudnt go from 0
population <- readxl::read_excel(here("data","population.xlsx"), skip=10) %>%
clean_names() %>%
group_by(x2,all_people) %>%
summarise () %>%
filter(!is.na(all_people))
filtered2_winter_data <- filtered_winter_data %>%
group_by(healthboards,bnf_item_code,paid_quantity,winter_year,gp_practice) %>%
summarise(total_paid = sum(paid_quantity, na.rm =TRUE))
filtered2_winter_data <- filtered2_winter_data %>%
cross_join(population)
SIMD
SIMD <- readxl::read_excel(here("data","SIMD.xlsx")) %>%
clean_names() # loading excel data
filtered_SIMD <- SIMD %>%
group_by(simd2020v2_quintile,h_bcode,h_bname) %>%
summarise()
filtered_SIMD <- filtered_SIMD %>%
rename(healthboards = h_bcode)
deprived_data <- filtered2_winter_data %>%
full_join(filtered_SIMD,by = "healthboards")
CHAT
#summarise totals per winter year per practice
winter_year_summary <- filtered2_winter_data %>%
group_by (healthboards, gp_practice, winter_year) %>%
summarise(total_paid = sum(paid_quantity, na.rm = TRUE))
# average across winter years
winter_year_average <- winter_year_summary %>%
group_by(healthboards,gp_practice) %>%
summarise(avg_paid_over_winters = mean(total_paid, na.rm = TRUE))
#SIMD
winter_with_simd <- winter_year_average %>%
left_join(filtered_SIMD, by="healthboards") %>%
filter(!is.na(simd2020v2_quintile))
CHAT2
box <- ggplot(winter_with_simd,
aes(x=factor(simd2020v2_quintile),
y=avg_paid_over_winters,
fill=factor(simd2020v2_quintile))) +
geom_boxplot() +
scale_fill_viridis(discrete=TRUE, alpha=0.6) +
geom_jitter(size=0.4, alpha=0.9) +
theme_minimal() +
labs(
title="Average Winter Antidepressant Prescriptions per Practice by SIMD Quintile",
x="SIMD Quintile (1 = Most Deprived)",
y="Avg prescriptions per practice (winter seasons)"
)
ggplotly(box)
questions : not sure the best way to displa my original data ? github - how to get rid of the signs 1- overall national trend (use original graph) 2- i want to show the variation between different regions using healthboards 3- link it to deprevation and look at prescriptions per person 4- can i do a map that shows pre covid and post covid side by side would that count as one 5- voilin plot across differet SIMDs to compare smaller unit of data - gp practice (postcode that links to SIMD) PATCHWORK - MAPS TOGETHETE reference line to show the split between precovid, covid and postcovid
every dot is a gp practice - gp practice - dataset - adressess (assessment prep) voilin plot if messy add transperency open data use quintiles for voilin plot do a code that says if not installed install and load packages